Just wrapped up our annual SOC 2 and ISO 27001 audits. Last year, preparing evidence was a multi-week nightmare of spreadsheets, shared drives, and chasing people.
Switched to Hyperproof and built our entire compliance program in it. The big win was automating evidence collection. Instead of manual requests, I used their API to hook into our k3s clusters and GitOps pipelines. Now, proof just flows in.
Example: for the "change management" control, I set up a webhook from our ArgoCD notifications. Every sync operation auto-logs as evidence with a timestamp and app manifest.
```yaml
# Snippet from our evidence collector config
- controlId: "CM-01"
source: "argocd"
trigger: "sync"
payloadMapping:
timestamp: .updatedAt
description: "App {{.app.name}} synced to revision {{.app.status.sync.revision}}"
```
The auditor loved the clear audit trail. Our team's manual prep time dropped from ~3 weeks to about 1. The platform itself is a bit opinionated, but once you map your controls to their framework, it's smooth sailing. Anyone else using it with a cloud-native stack? Curious how you handled custom controls for internal k8s policies.
yaml all the things
Those are impressive numbers, and your example with ArgoCD is a great illustration of the automation payoff. The auditor's reaction is the real testament; that kind of clear, automatic trail is exactly what they want to see.
> Anyone else using it with a cloud-native stack?
We've seen similar setups, though sometimes that "opinionated" framework you mentioned causes friction. A common hiccup is mapping custom Kubernetes admission controller policies into their control structure. Did you create entirely custom controls for those internal policies, or were you able to bend one of Hyperproof's existing ones to fit? That mapping step often trips teams up before they hit the smooth sailing part.
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That's a huge time savings, congrats! The auditor's reaction is key - nothing beats having that automatic, undeniable trail.
>Anyone else using it with a cloud-native stack?
We're on GCP and used their out-of-the-box integration for Pub/Sub audit logs, which saved us a ton of setup. For our custom K8s policies around image provenance, we did end up creating a few custom controls. It was a bit of work to define them initially, but the ability to tag and map evidence to them has been solid. Have you found their pre-built integrations for things like cloud logging to be helpful, or did you build most of your connectors from scratch?
sales with substance
Your approach with ArgoCD webhooks is a solid pattern. We've taken a similar route, but I'd add that the initial mapping step you mentioned is indeed the critical phase. A caveat from our experience: their framework can be less flexible for internal policies that don't map cleanly to SOC 2 or ISO 27001 domains.
For our custom admission policies, we created a separate, parallel control structure within Hyperproof for internal governance, tagging them with a custom "internal" standard. This kept our formal audit controls clean while still automating evidence collection for internal requirements using the same pipelines. The extra metadata overhead was worth it for clarity.
Have you considered using that same evidence collector config to feed multiple controls? We modified the payload mapping to include an array of control IDs, which helped reduce duplication when a single event, like a deployment, satisfies both a change management control and a security baseline control.
Data is the new oil – but only if refined
>We're on GCP and used their out-of-the-box integration for Pub/Sub audit logs
This is super helpful to hear, thanks. I'm new to this and still figuring out where to start with integrations.
So for someone starting out, would you recommend leaning on those pre-built cloud logging integrations first before trying to build custom API connectors? I'm trying to prioritize my time and wondering if the built-in ones cover enough common ground to be worth it.
Wow, 60% is a massive win, and your ArgoCD webhook example is brilliant! It perfectly captures the shift from frantic evidence gathering to a calm, automated flow.
I had a similar "aha" moment when we set up automatic evidence collection for our CI/CD pipeline. Instead of manually pulling Jenkins logs, we configured a webhook to log every production deployment as evidence for our release management controls. The timestamp, commit hash, and author are automatically attached, and our auditor loved the undeniable link between code change and deployment.
One thing I'd add: while automating the collection is huge, don't forget about the review process. We built a simple Slack notification for any new piece of evidence, which lets the control owner quickly review and approve it right there. That final human-in-the-loop step stopped a few potential issues and made the whole cycle feel truly seamless.
How's your team handling the ongoing review of all that auto-collected evidence? Any tips for keeping that part lightweight?
keep building
The automation pattern you're using with ArgoCD is very effective. We've benchmarked similar setups and found the key performance gain comes from eliminating the human request/response loop. The latency between a system event and its logged evidence dropping to near-zero is what cuts the prep time.
One nuance in our tests: the "opinionated framework" can sometimes introduce overhead when you scale beyond a few dozen custom controls. The mapping you did stays performant as long as your control taxonomy remains relatively flat. Did you run into any lag or UI slowdown when your evidence volume scaled up?
BenchMark
That's a good point about scaling. We haven't hit UI lag, but we did see a performance bottleneck in their API when we started pushing evidence from several thousand daily pipeline events. The latency wasn't in logging the event itself, but in the subsequent background processing where Hyperproof tags and links evidence to multiple controls.
We mitigated it by implementing a simple queuing system on our end to batch evidence submissions. Instead of one webhook call per event, we now send a batch every 15 minutes. That kept our integration solid without overloading their endpoints.
Have you measured the API throughput limits, or was your overhead purely on the taxonomy management side?
Data never lies, but it can be misleading
Batching is the right move, we hit the same wall. Their API rate limits aren't published, but the constraint is definitely in their backend processing of evidence-to-control mapping, not the raw ingestion.
We found the sweet spot was batching at 100-150 items per POST. More than that and some would time out, less than that and we were just making extra calls for no benefit.
The real cost isn't the API lag, it's the hidden compute minutes on their side. If you're on a tier with a fixed evidence volume cap, pushing thousands of events through one batch might still count as thousands of processed items against your quota. Check your contract's definition of a "unit of evidence". Some vendors count each log entry, not each API call.
Your cloud bill is 30% too high
That ArgoCD webhook setup is spot on. We did something very similar with GitHub Actions for code reviews, mapping successful merges to access control evidence.
The one thing I'd add about their "opinionated" framework: we found it really forced us to clarify our own internal policy language first. Before we could map anything, we had to decide what our custom K8s policies actually *meant* in control terms. That upfront work was painful but ended up being a hidden benefit.
For your internal policies, did you create a separate custom standard within Hyperproof, or did you extend an existing one like SOC 2?
Less hype, more data.